The non-contact detection of human vital signs (i.e., respiration rate (RR) and heartbeat rate (HR)) using a continuous-wave (CW) Doppler radar sensor has great potential for intensive care monitoring, home healthcare, etc. However, large-scale and fast random body movement (RBM) has been a bottleneck for vital sign detection using a single CW Doppler radar. To break this dilemma, this study proposed a scheme combining adaptive noise cancellation (ANC) with polynomial fitting, which could retrieve the weak components of both respiration and heartbeat signals that were submerged under serious RBM interference. In addition, the new-type discrete cosine transform (N-DCT) was introduced to improve the detection accuracy. This scheme was first verified using a numerical simulation. Then, experiments utilizing a 10-GHz Doppler radar sensor that was built from general-purpose radio frequency (RF) and communication instruments were also carried out. No extra RF/microwave components and modules were needed, and neither was a printed circuit board nor an integrated-chip design required. The experimental results showed that both the RR and HR could still be extracted during large-scale and fast body movements using only a single Doppler radar sensor because the RBM noises could be greatly eliminated by utilizing the proposed ANC algorithm.
To enhance the vital sign radar's detection accuracy of heartbeat rate (HR), which is heavily affected by the harmonics of respiration frequency, this paper proposes a waveform-driven matched filtering method based on polynomial fitting extraction. The merit of this approach lies in that, the quasi-ideal matching impulse approximating the actual heartbeat signal can be readily retrieved by subtracting the polynomial fitted waveform from a received signal, which provides high adaptability for individual subjects. Essentially, this extraction greatly removes the harmonic interference originated from respiration, thus considerably improving the HR detection accuracy. Simulations are performed to acquire the proper fitting order and the effective impulse duration. Guided by these two parameters, a 10-GHz non-contact continuous-wave (CW) Doppler radar system with typical microwave laboratory instruments is constructed. Experimental results confirmed the effectiveness of the proposed method, showing that the average errors of HR can be reduced from 46.21% to 0.96% under different subjects and distances, and from 38.05% to 0.90% in continuous measurement for one subject.
PurposeThis study examines the impact of the $60 billion tariff announcement of the US government on the Chinese exporting firms. In particular, it focuses on the firms whose revenues are highly dependent on the US economy.Design/methodology/approachThis study uses an experimental analysis and the event study methodology. The sample includes firms listed in mainland China and Hong Kong Stock Exchanges that have the highest revenues from exporting to the USA. The data are obtained from China Stock Market and Accounting Research (CSMAR) and DataStream.FindingsThe authors find that the tariff announcement has significantly negative impacts on stock performance both before and after the announcement, and the impacts are heterogeneous across all sample firms. For A shares listed in Mainland China, firms with more revenues from the US experience greater price drops on the announcement day, regardless of being in the targeted industry or not. But such finding is absent from H shares listed in Hong Kong. The authors also find that for all the firms, greater pricing power can alleviate the impacts of the tariff announcement.Research limitations/implicationsThe results provide implications to investors, policymakers and regulators on the further US-China cooperation in the future.Originality/valueThis is the first study documenting the heterogeneity of the impact of the tariff announcement and thus contributes to the prosperous studies on the varied firm-level responses in the Chinese stock market, and to the burgeoning literature by filling the gap of the financial market responses to the protectionist policy announcement.
In this paper, a concurrent dual-band hybrid down conversion architecture based on continuous-wave (CW) Doppler radar is demonstrated. The proposed system operates at 2.05-/1.64-GHz simultaneously. The dual-band can solve the null detection point problem generated at quarter-wavelength distance between the target and antenna. The detection results from different channels can be mutually verified to improve the system accuracy. The vital sign can be detected by the radar through the wooden board at higher transmit power. For satisfying the requirements of dual-band RF transmitters, and a two-stage dual-band power amplifier is designed. Experimental results have demonstrated the feasibility of the system.
Real-time monitoring of heart rate (HR), i.e., extraction of heart rate variability (HRV), plays an important role in diagnosis and prevention of cardiovascular diseases. Compared with traditional contact monitoring devices, the use of continuous wave (CW) Doppler radar to monitor HRV does not require contact and is not sensitive to light and temperature, which makes it more and more popular. To monitor the HRV based on CW Doppler radar, the time window must be shortened to less than 5 s, which will lead to the spectrum leakage and degrade the measurement accuracy of HRV. To solve this problem, a custom CW Doppler radar has been developed in an integrated fashion on a single PCB, whose transmitting frequency and power of the radar are 24 GHz and 3 dBm, respectively. Furthermore, four frequency interpolation algorithms are introduced to compare their extraction accuracy. Experiments are performed on three subjects, and results show that the Quinn algorithm can obtain best HRV extraction results compared with other algorithms. Specially, the average HRV extraction error is 3.61% using the Quinn algorithm.
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